elif level == 1: self.compress_level_0 = Conv( int(1024*multiplier), self.inter_dim, 1, 1) self.stride_level_2 = Conv( int(256*multiplier), self.inter_dim, 3, 2) self.expand = Conv(self.inter_dim, int(512*multiplier), 3, 1)

class Partial_conv3(nn.Module): def init(self, dim, n_div, forward): super().init() self.dim_conv3 = dim // n_div self.dim_untouched = dim - self.dim_conv3 self.partial_conv3 = nn.Conv2d(self.dim_conv3, self.dim_conv3, 3, 1, 1, bias=False) if forward == 'slicing': self.forward = self.forward_slicing elif forward == 'split_cat': self.forward = self.forward_split_cat else: raise NotImplementedError def forward_slicing(self, x: Tensor) -> Tensor: # only for inference x = x.clone() # !!! Keep the original input intact for the residual connection later x[:, :self.dim_conv3, :, :] = self.partial_conv3(x[:, :self.dim_conv3, :, :]) return x def forward_split_cat(self, x: Tensor) -> Tensor: x1, x2 = torch.split(x, [self.dim_conv3, self.dim_untouched], dim=1) x1 = self.partial_conv3(x1) x = torch.cat((x1, x2), 1) return x 在这段代码中插入全局平均池化做通道增强的模块

self.partial_conv3 = nn.Conv2d(self.dim_conv3, self.dim_conv3, 3, 1, 1, bias=False) self.global_avg_pool = nn.AdaptiveAvgPool2d((1, 1)) # 添加全局平均池化层 if forward == 'slicing': self.forward =...

class Partial_conv3(nn.Module): def init(self, dim, n_div, forward): super().init() self.dim_conv3 = dim // n_div self.dim_untouched = dim - self.dim_conv3 self.partial_conv3 = nn.Conv2d(self.dim_conv3, self.dim_conv3, 3, 1, 1, bias=False) self.global_pool = GlobalAvgPool2d() if forward == 'slicing': self.forward = self.forward_slicing elif forward == 'split_cat': self.forward = self.forward_split_cat else: raise NotImplementedError def forward_slicing(self, x: Tensor) -> Tensor: # only for inference x = x.clone() # !!! Keep the original input intact for the residual connection later x[:, :self.dim_conv3, :, :] = self.partial_conv3(x[:, :self.dim_conv3, :, :]) return x def forward_split_cat(self, x: Tensor) -> Tensor: x1, x2 = torch.split(x, [self.dim_conv3, self.dim_untouched], dim=1) x1 = self.partial_conv3(x1) x1 = self.global_pool(x1) x = torch.cat((x1, x2), 1) return x在这段代码中插入全局平均池化做通道增强的模块

self.partial_conv3 = nn.Conv2d(self.dim_conv3, self.dim_conv3, 3, 1, 1, bias=False) self.global_pool = GlobalAvgPool2d() self.channel_enhance = nn.Sequential( nn.Conv2d(dim, dim, kernel_size=1, ...

可以帮我修改一下这段代码吗 class Vacuum: definit(self): self.location = [0,0] # 真空吸尘器的初始位置 self.direction = 'N' # 真空吸尘器的朝向 def sense(self, environment): # 读取传感器数据 return environment[self.location[0]][self.location[1]] def act(self, action): # 执行动作 if action == 'move': if self.direction == 'N': self.location[0] -= 1 elif self.direction == 'S': self.location[0] += 1 elif self.direction == 'W': self.location[1] -= 1 elif self.direction == 'E': self.location[1] += 1 elif action == 'turn_left': if self.direction == 'N': self.direction = 'W' elif self.direction == 'S': self.direction = 'E' elif self.direction == 'W': self.direction = 'S' elif self.direction == 'E': self.direction = 'N' elif action == 'turn_right': if self.direction == 'N': self.direction = 'E' elif self.direction == 'S': self.direction = 'W' elif self.direction == 'W': self.direction = 'N' elif self.direction == 'E': self.direction = 'S' elif action == 'clean': # 吸尘 pass def update(self, environment): # 更新真空吸尘器的状态 pass def main(): # 初始化真空吸尘器和环境 vacuum = Vacuum() environment = [[0, 1, 0], [0, 0, 0], [0, 0, 0]] # 启动真空吸尘器 while True: sensor_data = vacuum.sense(environment) action = reflex_model(sensor_data) vacuum.act(action) vacuum.update(environment)

return environment[self.location[0]][self.location[1]] def act(self, action): # 执行动作 if action == 'move': if self.direction == 'N': self.location[0] -= 1 elif self.direction == 'S': self....

def add_logic(self,row,col,color): if self.direct == 0: if 0 < row < self.__n-1 : self.logic_board_state[row * 4 + col][0] = color self.logic_board_state[row * 4 + col - 4][1] = color elif row == 0: self.logic_board_state[col][0] = color elif row == self.__n-1 : self.logic_board_state[row3+col][1] = color elif self.direct == 1: if 0 < col < self.__n-1 : self.logic_board_state[row4 + col][2] = color self.logic_board_state[row4 + col - 1][3] = color elif col == 0: self.logic_board_state[row4][2] = color elif col == self.__n-1: self.logic_board_state[4*row + col - 1][3] = color

如果是水平方向，且行等于 self.__n-1，则只更新最后一列的状态，即 self.logic_board_state[row*3+col][1]。如果是垂直方向，且列的范围在 1 到 self.__n-2 之间（不包括边界），则更新两个位置的状态。...

class Partial_conv3(nn.Module): def init(self, dim, n_div, forward): super().init() self.dim_conv3 = dim // n_div self.dim_untouched = dim - self.dim_conv3 self.partial_conv3 = nn.Conv2d(self.dim_conv3, self.dim_conv3, 3, 1, 1, bias=False) self.global_pool = GlobalAvgPool2d() if forward == 'slicing': self.forward = self.forward_slicing elif forward == 'split_cat': self.forward = self.forward_split_cat else: raise NotImplementedError def forward_slicing(self, x: Tensor) -> Tensor: # only for inference x = x.clone() # !!! Keep the original input intact for the residual connection later x[:, :self.dim_conv3, :, :] = self.partial_conv3(x[:, :self.dim_conv3, :, :]) return x def forward_split_cat(self, x: Tensor) -> Tensor: x1, x2 = torch.split(x, [self.dim_conv3, self.dim_untouched], dim=1) x1 = self.global_pool(x1) x1 = self.partial_conv3(x1) x = torch.cat((x1, x2), 1) return x这段代码有什么错误

self.partial_conv3 = nn.Conv2d(self.dim_conv3, self.dim_conv3, 3, 1, 1, bias=False) self.global_pool = GlobalAvgPool2d() self.forward = None if forward == 'slicing': self.forward = self.forward_...

修改以下代码使其能够输出模型预测结果： def open_image(self): file_dialog = QFileDialog() file_paths, _ = file_dialog.getOpenFileNames(self, "选择图片", "", "Image Files (.png .jpg .jpeg)") if file_paths: self.display_images(file_paths) def preprocess_images(self, image_paths): data_transform = transforms.Compose([ transforms.CenterCrop(150), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) self.current_image_paths = [] images = [] for image_path in image_paths: image = Image.open(image_path) image = data_transform(image) image = torch.unsqueeze(image, dim=0) images.append(image) self.current_image_paths.append(image_path) return images def predict_images(self): if not self.current_image_paths: return for i, image_path in enumerate(self.current_image_paths): image = self.preprocess_image(image_path) output = self.model(image) predicted_class = self.class_dict[output.argmax().item()] self.result_labels[i].setText(f"Predicted Class: {predicted_class}") self.progress_bar.setValue((i+1)20) def display_images(self, image_paths): for i, image_path in enumerate(image_paths): image = QImage(image_path) image = image.scaled(300, 300, Qt.KeepAspectRatio) if i == 0: self.image_label_1.setPixmap(QPixmap.fromImage(image)) elif i == 1: self.image_label_2.setPixmap(QPixmap.fromImage(image)) elif i == 2: self.image_label_3.setPixmap(QPixmap.fromImage(image)) elif i == 3: self.image_label_4.setPixmap(QPixmap.fromImage(image)) elif i == 4: self.image_label_5.setPixmap(QPixmap.fromImage(image))

file_paths, _ = file_dialog.getOpenFileNames(self, "选择图片", "", "Image Files (*.png *.jpg *.jpeg)") if file_paths: self.display_images(file_paths) def preprocess_images(self, image_paths): ...

if int(i) == 1: Start.start_total() elif int(i) == 2: Start.repeat_management_test01() elif int(i) == 3: Start.repeat_management_test02() elif int(i) == 4: Start.repeat_management_test03() elif int(i) == 5: Start.repeat_management_test04() else: Start.repeat_management_test05() 当我i=1时，执行完1后，怎么写继续执行测试，再次选择测试项

if int(i) == 1: Start.start_total() elif int(i) == 2: Start.repeat_management_test01() elif int(i) == 3: Start.repeat_management_test02() elif int(i) == 4: Start.repeat_management_test03() ...

def init(self,player,groups): super().init(groups) self.sprite_type = 'weapon' direction = player.status.split('_')[0] # graphic full_path = f'../graphics/weapons/{player.weapon}/{direction}.png' self.image = pygame.image.load(full_path).convert_alpha() # placement if direction == 'right': self.rect = self.image.get_rect(midleft = player.rect.midright + pygame.math.Vector2(0,16)) elif direction == 'left': self.rect = self.image.get_rect(midright = player.rect.midleft + pygame.math.Vector2(0,16)) elif direction == 'down': self.rect = self.image.get_rect(midtop = player.rect.midbottom + pygame.math.Vector2(-10,0)) else: self.rect = self.image.get_rect(midbottom = player.rect.midtop + pygame.math.Vector2(-10,0))对该代码进行注释

self.rect = self.image.get_rect(midleft=player.rect.midright + pygame.math.Vector2(0, 16)) elif direction == 'left': self.rect = self.image.get_rect(midright=player.rect.midleft + pygame.math....

def __choose_button(self, x, y): """ 根据用户控制执行游戏逻辑，重新开始游戏，退出游戏，或者在棋盘内落子：param x：鼠标横坐标：param y：鼠标纵坐标：return：无 """ pass left = self.__cell_width * self.n+40 right=left + 150 if left < x < right: if 370 < y < 420: #重新开始 self.start() elif 300 < y < 350: pygame.quit() sys.exit() elif 230 < y < 280: self.pvp() elif 160 < y < 210: self.first() elif 0 < x < self.cell_width(self.n-1) + self.margin2: self.__next_step(x, y) def valide(self, row, col, direct): if direct== 0: if 0 <= row <= (self.n-1) and 0 <= col <= (self.n-2): if 0 <= row <(self.__n-1): return self.logic_board_state[row(self.__n-1)+col][0] == 0 else: return self.logic_board_state[(row-1)(self.n-1)+col][1] == 0 else: return False elif direct == 1: if 0 <= col <= (self.n-1) and 0 <= row <= (self.n-1): if 0 <= col < (self.n-1): return self.logic_board_state[row(self.__n-1)+col][2] == 0 else: return self.logic_board_state[row(self.__n-1)+col-1][3] == 0 else: return False

这是两个方法的实现。 __choose_button 方法用于根据用户的输入控制游戏的逻辑。...该方法没有返回值。 valide 方法用于检查给定的行、列和方向是否是有效的落子位置。根据给定的 row、col 和 direct 参数...

请仿照这一段代码，if self.type == "csv": try: self.pd_data = pd.read_csv(self.file_path) except: raise ValueError('csv路径错误') elif self.type == "txt": self.pd_data = pd.DataFrame( pd.read_table(self.file_path, sep=' ', header=None, engine='python', names=self.names)) elif self.type == "pmlb": try: self.pd_data = fetch_data(str(self.file_path), local_cache_dir='./datasets', return_X_y=False) self.x, self.y = fetch_data(str(self.file_path), local_cache_dir='./datasets', return_X_y=True) except: raise ValueError('pmlb数据集名称错误') elif self.type == "numpy": try: self.pd_data = pd.DataFrame(self.file_path) self.x, self.y = pd.DataFrame(self.file_path[:, :-1]), pd.DataFrame(self.file_path[:, -1]) except: raise ValueError('numpy数据集设置失败')

self.x, self.y = pd.DataFrame(self.file_path[:, :-1]), pd.DataFrame(self.file_path[:, -1]) except: raise ValueError('numpy数据集设置失败') 在上面的示例中，我们创建了一个名为DataReader的类，它...

def ai_game(self): board = 0 state = 0 for i in range((self.n-1)(self.n-1)): if self.logic_board_state[i].count(int(0)) > 2 : if self.logic_board_state[i][0] == 0 : self.direct = 0 board=i state=1 elif self.logic_board_state[i][1] == 0 : self.direct = 0 board = i state = 0 elif self.logic_board_state[i][2] == 0 : self.direct= 1 board = i state = 3 elif self.logic_board_state[i][3] == 0 : self.direct = 1 board = i state = 2 elif self.logic_board_state[i].count(int(0)) == 1 : num = self.logic_board_state[i].index(0) if num <= 1 : self.direct = 0 else: self.direct = 1 board = i state = num if self.ai_vilide(board,state): return [board, state] else: continue for i in range((self.n - 1) (self.n - 1)): if self.logic_board_state[i].count(int(0)) == 2: num = self.logic_board_state[i].index(0) if num <= 1: self.direct = 0 else: self.direct = 1 board = i state = num if self.ai_vilide(board, state): return [board, state] else: continue

这段代码是一个AI玩家的游戏逻辑。它通过遍历棋盘的每个格子，根据不同的情况选择一个可以落子的位置。首先，它检查每个格子中有多少个空位，如果有大于2个空位，就优先选择空位最多的那个位置进行落子，并记录该...

优化代码 def GetAlgType(self, AlgType): if AlgType == "SGD_SM1": AlgType = self.AlgType.SGD_SM1 elif AlgType == "SGD_SM4": AlgType = self.AlgType.SGD_SM4 elif AlgType == "SGD_DES": AlgType = self.AlgType.SGD_DES elif AlgType == "SGD_2DES": AlgType = self.AlgType.SGD_2DES elif AlgType == "SGD_3DES": AlgType = self.AlgType.SGD_SM4 elif AlgType == "SGD_AES": AlgType = self.AlgType.SGD_AES elif AlgType == "SGD_AES192": AlgType = self.AlgType.SGD_AES192 elif AlgType == "SGD_AES256": AlgType = self.AlgType.SGD_AES256 return AlgType

可以优化代码，将多个if-elif语句改为使用字典来映射AlgType值。这样可以提高代码的可读性和性能。修改后的代码如下： def GetAlgType(self, AlgType): alg_mapping = { "SGD_SM1": self.AlgType.SGD_SM1, "SGD_...

def judge_owner(self,row,col,color,direct): c=[] if direct == 0: if 0 < row < self.__n-1: if 0 not in self.logic_board_state[row * 4 + col]: self.logic_board_owner[row * 4 + col] = color c.append(row * 4 + col) if 0 not in self.logic_board_state[row * 4 + col - 4]: self.logic_board_owner[row * 4 + col - 4] = color c.append(row * 4 + col - 4) elif row == 0: if 0 not in self.logic_board_state[col]: self.logic_board_owner[col] = color c.append(col) elif row == self.__n - 1: if 0 not in self.logic_board_state[row3+col]: self.logic_board_owner[row3+col] = color c.append(row3+col) if direct == 1: if 0 < col < (self.__n-1): if 0 not in self.logic_board_state[row4 + col]: self.logic_board_owner[row4 + col] = color c.append(row4 + col) if 0 not in self.logic_board_state[row4 + col - 1]: self.logic_board_owner[row4 + col - 1] = color c.append(row4 + col - 1) elif col == 0: if 0 not in self.logic_board_state[row4]: self.logic_board_owner[row4] = color c.append(row4) elif col == self.__n - 1: if 0 not in self.logic_board_state[4row + (col - 1)]: self.logic_board_owner[4row + (col - 1)] = color c.append(4*row + (col - 1)) return c

如果满足条件 0 < row < self.__n-1，会执行以下操作： - 检查 self.logic_board_state[row * 4 + col] 中是否没有 0。如果没有，将 self.logic_board_owner[row * 4 + col] 设置为 color，并将 row * 4 + ...

def valide(self, row, col, direct): if direct== 0: if 0 <= row <= (self.n-1) and 0 <= col <= (self.n-2): if 0 <= row <(self.__n-1): return self.logic_board_state[row(self.__n-1)+col][0] == 0 else: return self.logic_board_state[(row-1)(self.n-1)+col][1] == 0 else: return False elif direct == 1: if 0 <= col <= (self.n-1) and 0 <= row <= (self.n-1): if 0 <= col < (self.n-1): return self.logic_board_state[row(self.__n-1)+col][2] == 0 else: return self.logic_board_state[row(self.__n-1)+col-1][3] == 0 else: return False

如果0 <= row (self.__n-1)，即不在最后一行，则判断对应位置上的元素是否为0。如果是0，则返回True，表示可以进行操作。否则，返回False。如果row的值为(self.__n-1)，即在最后一行，则判断对应位置上的元素是否...

elif level == 1: self.compress_level_0 = Conv( int(1024multiplier), self.inter_dim, 1, 1) self.stride_level_2 = Conv( int(256multiplier), self.inter_dim, 3, 2) self.expand = Conv(self.inter_dim, int(512*multiplier), 3, 1)

elif level == 2: self.compress_level_0 = Conv( int(1024multiplier), self.inter_dim, 1, 1) self.compress_level_1 = Conv( int(512multiplier), self.inter_dim, 1, 1) self.expand = Conv(self.inter_dim, int( 256*multiplier), 3, 1)

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elif level == 1: self.compress_level_0 = Conv( int(1024*multiplier), self.inter_dim, 1, 1) self.stride_level_2 = Conv( int(256*multiplier), self.inter_dim, 3, 2) self.expand = Conv(self.inter_dim, int(512*multiplier), 3, 1)

elif level == 2: self.compress_level_0 = Conv( int(1024*multiplier), self.inter_dim, 1, 1) self.compress_level_1 = Conv( int(512*multiplier), self.inter_dim, 1, 1) self.expand = Conv(self.inter_dim, int( 256*multiplier), 3, 1)

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elif level == 1: self.compress_level_0 = Conv( int(1024multiplier), self.inter_dim, 1, 1) self.stride_level_2 = Conv( int(256multiplier), self.inter_dim, 3, 2) self.expand = Conv(self.inter_dim, int(512*multiplier), 3, 1)

elif level == 2: self.compress_level_0 = Conv( int(1024multiplier), self.inter_dim, 1, 1) self.compress_level_1 = Conv( int(512multiplier), self.inter_dim, 1, 1) self.expand = Conv(self.inter_dim, int( 256*multiplier), 3, 1)